mlsanityData to 7 Oct 2026

Decoupling "when to update" from "how to update"

arXiv 1706.02613 ↗First posted 8 Jun 2017cs.LGcited by 230

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Abstract (arXiv)

Deep learning requires data. A useful approach to obtain data is to be creative and mine data from various sources, that were created for different purposes. Unfortunately, this approach often leads to noisy labels. In this paper, we propose a meta algorithm for tackling the noisy labels problem. The key idea is to decouple "when to update" from "how to update". We demonstrate the effectiveness of our algorithm by mining data for gender classification by combining the Labeled Faces in the Wild (LFW) face recognition dataset with a textual genderizing service, which leads to a noisy dataset. While our approach is very simple to implement, it leads to state-of-the-art results. We analyze some convergence properties of the proposed algorithm.

Key facts. Cited by 230 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jun 2017.

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Citing papers per year: 2021: 68; 2022: 60; 2023: 42; 2024: 32; 2025: 16; 2026: 12.

230 papers.

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  1. 2021Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspectivecited by 116
  2. 2022Selective-Supervised Contrastive Learning with Noisy Labelscited by 112
  3. 2021Provably End-to-end Label-Noise Learning without Anchor Pointscited by 97
  4. 2021Augmentation Strategies for Learning with Noisy Labelscited by 86
  5. 2021Jo-SRC: A Contrastive Approach for Combating Noisy Labelscited by 80
  6. 2021Sample Selection with Uncertainty of Losses for Learning with Noisy Labelscited by 79
  7. 2022UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learningcited by 77
  8. 2021NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphscited by 73
  9. 2022Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognitioncited by 67
  10. 2021Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularizationcited by 62
  11. 2021Open-set Label Noise Can Improve Robustness Against Inherent Label Noisecited by 58
  12. 2021NGC: A Unified Framework for Learning with Open-World Noisy Datacited by 58
38 more
  1. 2021Co-learning: Learning from Noisy Labels with Self-supervisioncited by 50
  2. 2021Instance-dependent Label-noise Learning under a Structural Causal Modelcited by 50
  3. 2021From Synthetic to Real: Unsupervised Domain Adaptation for Animal Pose Estimationcited by 48
  4. 2021Hard Sample Aware Noise Robust Learning for Histopathology Image Classificationcited by 48
  5. 2022Investigating Why Contrastive Learning Benefits Robustness Against Label Noisecited by 46
  6. 2023When Source-Free Domain Adaptation Meets Learning with Noisy Labelscited by 46
  7. 2021LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environmentcited by 40
  8. 2022Mitigating Memorization of Noisy Labels by Clipping the Model Predictioncited by 40
  9. 2021Faster Meta Update Strategy for Noise-Robust Deep Learningcited by 38
  10. 2021Rethinking Graph Auto-Encoder Models for Attributed Graph Clusteringcited by 37
  11. 2022Towards Robust Ranker for Text Retrievalcited by 37
  12. 2021Towards Understanding Deep Learning from Noisy Labels with Small-Loss Criterioncited by 36
  13. 2021Multimodal Co-learning: Challenges, Applications with Datasets, Recent Advances and Future Directionscited by 35
  14. 2021Learning from Noisy Labels for Entity-Centric Information Extractioncited by 34
  15. 2021Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approachcited by 32
  16. 2021Boosting Co-teaching with Compression Regularization for Label Noisecited by 31
  17. 2021Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Networkcited by 30
  18. 2022PCL: Peer-Contrastive Learning with Diverse Augmentations for Unsupervised Sentence Embeddingscited by 29
  19. 2022Joint-Modal Label Denoising for Weakly-Supervised Audio-Visual Video Parsingcited by 29
  20. 2022Robust Training of Graph Neural Networks via Noise Governancecited by 29
  21. 2023Twin Contrastive Learning with Noisy Labelscited by 28
  22. 2021Continual Learning on Noisy Data Streams via Self-Purified Replaycited by 27
  23. 2023BiCro: Noisy Correspondence Rectification for Multi-modality Data via Bi-directional Cross-modal Similarity Consistencycited by 27
  24. 2022Towards Open Set Video Anomaly Detectioncited by 26
  25. 2022Towards Federated Learning against Noisy Labels via Local Self-Regularizationcited by 26
  26. 2021Superpixel-guided Iterative Learning from Noisy Labels for Medical Image Segmentationcited by 25
  27. 2022CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learningcited by 24
  28. 2022Neighborhood Collective Estimation for Noisy Label Identification and Correctioncited by 22
  29. 2023Adaptive Integration of Partial Label Learning and Negative Learning for Enhanced Noisy Label Learningcited by 22
  30. 2021Unified Robust Training for Graph NeuralNetworks against Label Noisecited by 21
  31. 2022From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Modelcited by 21
  32. 2022Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-trainingcited by 21
  33. 2021Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactionscited by 20
  34. 2021Adaptive Sample Selection for Robust Learning under Label Noisecited by 20
  35. 2021Understanding the Interaction of Adversarial Training with Noisy Labelscited by 19
  36. 2023MDCS: More Diverse Experts with Consistency Self-distillation for Long-tailed Recognitioncited by 19
  37. 2021Two Wrongs Don't Make a Right: Combating Confirmation Bias in Learning with Label Noisecited by 18
  38. 2021PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy Labelscited by 17

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